Move tests to separate folder

tests/test_extractors.py

@@ -0,0 +1,69 @@
+#  MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
+#  Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
+#  Released under the modified BSD license. See COPYING.md for more details.
+import numpy as np
+
+from miplearn.extractors import (
+    SolutionExtractor,
+    InstanceFeaturesExtractor,
+    VariableFeaturesExtractor,
+)
+from miplearn.problems.knapsack import KnapsackInstance
+from miplearn.solvers.learning import LearningSolver
+
+
+def _get_instances():
+    instances = [
+        KnapsackInstance(
+            weights=[1.0, 2.0, 3.0],
+            prices=[10.0, 20.0, 30.0],
+            capacity=2.5,
+        ),
+        KnapsackInstance(
+            weights=[3.0, 4.0, 5.0],
+            prices=[20.0, 30.0, 40.0],
+            capacity=4.5,
+        ),
+    ]
+    models = [instance.to_model() for instance in instances]
+    solver = LearningSolver()
+    for (i, instance) in enumerate(instances):
+        solver.solve(instances[i], models[i])
+    return instances, models
+
+
+def test_solution_extractor():
+    instances, models = _get_instances()
+    features = SolutionExtractor().extract(instances)
+    assert isinstance(features, dict)
+    assert "default" in features.keys()
+    assert isinstance(features["default"], np.ndarray)
+    assert features["default"].shape == (6, 2)
+    assert features["default"].ravel().tolist() == [
+        1.0,
+        0.0,
+        0.0,
+        1.0,
+        1.0,
+        0.0,
+        1.0,
+        0.0,
+        0.0,
+        1.0,
+        1.0,
+        0.0,
+    ]
+
+
+def test_instance_features_extractor():
+    instances, models = _get_instances()
+    features = InstanceFeaturesExtractor().extract(instances)
+    assert features.shape == (2, 3)
+
+
+def test_variable_features_extractor():
+    instances, models = _get_instances()
+    features = VariableFeaturesExtractor().extract(instances)
+    assert isinstance(features, dict)
+    assert "default" in features
+    assert features["default"].shape == (6, 5)